Sensory and Physicochemical Evaluation of Dairy‐Like Plant‐Based Milk Formulations Predicted by Machine Learning

), followed by Formulation 2 and pasteurized cow (1.04 µm), with Formulation 1 (1.62 µm) as the largest. The UHT and pasteurized cow milk had higher whiteness indices compared with Formulation 1 and Formulation 2. Formulations exhibited faster sedimentation compared to cow milk. The models correctly predicted viscosity and some volatiles. However, predictive performance was limited by the small dataset and restricted access to commercial formulations. A larger dataset and access to formulations would improve model accuracy. PRACTICAL APPLICATIONS: This research demonstrates the potential of predictive modelling for the development of plant-based alternatives to animal-derived ingredients. Such an approach would reduce reliance on trial and error in product development, saving time and development costs while meeting consumer needs.

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Publication Details

Journal
Journal of Food Science
Published
2026-05-01
DOI
https://doi.org/10.1111/1750-3841.71104
Primary Topic
Proteins in Food Systems
Type
article
Field-Weighted Citation Impact
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article

Sensory and Physicochemical Evaluation of Dairy‐Like Plant‐Based Milk Formulations Predicted by Machine Learning

Sonja Kukuljan, R. Keast, Nelum Pematilleke, Joanna M. Gambetta et al.
Journal of Food Science
Proteins in Food Systems
article

Sensory and Physicochemical Evaluation of Dairy‐Like Plant‐Based Milk Formulations Predicted by Machine Learning

Sonja Kukuljan, R. Keast, Nelum Pematilleke, Joanna M. Gambetta, Amy Logan, Anesu A. Magwere
article en

Abstract

), followed by Formulation 2 and pasteurized cow (1.04 µm), with Formulation 1 (1.62 µm) as the largest. The UHT and pasteurized cow milk had higher whiteness indices compared with Formulation 1 and Formulation 2. Formulations exhibited faster sedimentation compared to cow milk. The models correctly predicted viscosity and some volatiles. However, predictive performance was limited by the small dataset and restricted access to commercial formulations. A larger dataset and access to formulations would improve model accuracy. PRACTICAL APPLICATIONS: This research demonstrates the potential of predictive modelling for the development of plant-based alternatives to animal-derived ingredients. Such an approach would reduce reliance on trial and error in product development, saving time and development costs while meeting consumer needs.

Journal of Food ScienceVol. 91(5)
Commonwealth Scientific and Industrial Research Organisation (AU), Deakin University (AU), Hunter Water (AU), Auburn Hospital (AU), Scientific Games (Australia) (AU), Dairy Innovation Australia (Australia) (AU)
Openalex Percentile: Top 6%
Proteins in Food Systems
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